TY - GEN
T1 - Machine Learning Based RATs Selection Supporting Multi-connectivity for Reliability (Invited Paper)
AU - Lee, Haeyoung
AU - Vahid, Seiamak
AU - Moessner, Klaus
N1 - © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2019
Y1 - 2019
N2 - While ultra-reliable and low latency communication (uRLLC) is expected to cater to emerging services requiring real-time control, such as factory automation and autonomous driving, the design of uRLLC of stringent requirements would be very challenging. Among novel solutions to satisfy uRLLC’s requirements, interface diversity is widely regarded as an efficient enabler of ultra-reliable connectivity. When mobile devices are connected to multiple base stations (BSs) of different radio access technologies (RATs) and same data is transmitted via multiple links simultaneously, the transmission reliability can be improved. However, duplicate transmission of same data causes an increase in the traffic loads, leading to radio resource shortage. Considering it, efficient configuration of multi-connectivity (MC) for mobile devices is important. In this paper, the RAT selection scheme including efficient MC configuration is proposed. By adopting distributed reinforcement learning (RL), each device could learn the policy for efficient MC configuration and select appropriate RATs. Simulation results show that 20.8% reliability improvements over the single connectivity scheme is observed. Comparing to the method to configure MC for devices all the time, 37.6% improvement is achieved at high traffic loads.
AB - While ultra-reliable and low latency communication (uRLLC) is expected to cater to emerging services requiring real-time control, such as factory automation and autonomous driving, the design of uRLLC of stringent requirements would be very challenging. Among novel solutions to satisfy uRLLC’s requirements, interface diversity is widely regarded as an efficient enabler of ultra-reliable connectivity. When mobile devices are connected to multiple base stations (BSs) of different radio access technologies (RATs) and same data is transmitted via multiple links simultaneously, the transmission reliability can be improved. However, duplicate transmission of same data causes an increase in the traffic loads, leading to radio resource shortage. Considering it, efficient configuration of multi-connectivity (MC) for mobile devices is important. In this paper, the RAT selection scheme including efficient MC configuration is proposed. By adopting distributed reinforcement learning (RL), each device could learn the policy for efficient MC configuration and select appropriate RATs. Simulation results show that 20.8% reliability improvements over the single connectivity scheme is observed. Comparing to the method to configure MC for devices all the time, 37.6% improvement is achieved at high traffic loads.
KW - Machine learning
KW - Multi-connectivity
KW - RAT selection
KW - URLLC
UR - http://www.scopus.com/inward/record.url?scp=85071496731&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-25748-4_3
DO - 10.1007/978-3-030-25748-4_3
M3 - Conference contribution
AN - SCOPUS:85071496731
SN - 9783030257477
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 31
EP - 41
BT - Cognitive Radio-Oriented Wireless Networks - 14th EAI International Conference, CrownCom 2019, Proceedings
A2 - Kliks, Adrian
A2 - Sybis, Michal
A2 - Kryszkiewicz, Pawel
A2 - Bader, Faouzi
A2 - Triantafyllopoulou, Dionysia
A2 - Caicedo, Carlos E.
A2 - Sezgin, Aydin
A2 - Dimitriou, Nikos
PB - Springer Nature Link
T2 - 14th EAI International Conference on Cognitive Radio-Oriented Wireless Networks, CROWNCOM 2019
Y2 - 11 June 2019 through 12 June 2019
ER -